Predicting hydrological fluxes in the Haihe river basin using remote sensing and data assimilation methods
In order to improve the prediction of actual evapotranspiration, groundwater recharge and soil water storage, remotely sensed data and data assimilation methods will be used. In an innovative approach we will focus on the assimilation of soil moisture and leaf area index (LAI) data and comprehensive validation activities on local, footprint and regional scale.
We will analyze the advantage of using ensemble Kalman filter (EnKF) and particle filter (PF). In addition to the state update of LAI and soil moisture, a parameter update of soil hydraulic properties and root water uptake is performed. By forward model runs virtual soils and regional hydrological systems are simulated to analyze the role of model errors from inaccurate process description and parameterization on the prediction of groundwater recharge and evapotranspiration.
Lysimeter installation in Huailai, China
The modeling results are validated by experimental studies within the Haihe river basin. On the local scale weighable lysimeters are used to determine groundwater recharge, soil water storage and evapotranspiration. On the footprint scale we determine actual evapotranspiration using eddy covariance systems (EC) and large aperture scintillometer (LAS) which will be compared to remotely sensed evapotranspiration values. To two regions in the Haihe river basin the data assimilation approach will be applied by using multiple remote sensing data.
The study aims to identify the need and the required accuracy of additional hydrological information that may contribute in constraining the uncertainties of the model parameter space and the model prediction uncertainty, above all over ungauged river basins or in case of reduced data availability. In addition, a monitoring of hydrological fluxes, in particular groundwater recharge and actual evapotranspiration, is performed which will be able to react to rapidly changing environmental conditions and to utilize future mission data.
Dr. Carsten Montzka
Prof. Harry Vereecken
Dr. Thomas Pütz